About healthiar 1/5

About healthiar 2/5

Figure: healthiar overview. CBA = Cost-benefit analysis; DALY = disability-adjusted life years; GBD = global burden of disease; MDI = multidimensional deprivation index; PAF = population attributable fraction; PIF = population impact fraction; YLD = years lived with disability; YLL = years of life lost

About healthiar 3/5

About healthiar 4/5

Selection of healthiar core family members (functions)

• attribute_health() to env. exposure with either relative or absolute risk
• compare() two scenarios
• summarize_uncertainty() Monte Carlo simulation
• monetize() health impacts
• …

healthiar in RStudio 1/2

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio 2/2

Any arguments without a = symbol after the name have no default and must be user-specified

Refresher - Burden of disease with relative risk

attribute_health() with RR

results_pm_copd <-
  attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", ...
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

results_pm_copd <- attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidence
  ) 

Output structure

How to access the results

Let’s inspect the main results

results_pm_copd$health_main

attribute_health() with RR & uncertainty

Goal: attribute COPD cases to PM2.5 exposure

results_pm_copd <- attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
) 

Let’s inspect the detailed results

results_pm_copd$health_detailed$impact_raw

summarize_uncertainty() for overall confidence intervals

results_pm_copd <- 
  summarize_uncertainty(
    results = results_pm_copd,
    n_sim = 1000
)

Let’s inspect the main results

results_pm_copd$uncertainty_main

Let’s inspect the detailed results

results_pm_copd$uncertainty_detailed

attribute_health() with RR & user-defined ERF

A user-defined exposure-response function can be fed to the erf_eq_... arguments

• Any function of the form intercept + a x c^1 + b x c^2 + …

• Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()

  • E.g. MR-BRT curves from Global Burden of Disease study

results_pm_copd_mr_brt <- attribute_health(
    exp_central = 8.85,
    bhd_central = 30747,
    erf_eq_central = 
      stats::splinefun(
        x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
        y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
        method = "natural"),
    cutoff_central = 0
)

Let’s inspect the main results

results_pm_copd_mr_brt$health_main

Refresher - Burden of disease with absolute risk

attribute_health() with AR

Goal: attribute cases of high annoyance (HA) to noise exposure

results_noise_ha <- attribute_health(
    approach_risk = "absolute_risk",
    exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
    pop_exp = c(387500, 286000, 191800, 72200, 7700),
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2")

Results per noise exposure band

results_noise_ha$health_detailed$impact_raw

Iteration with attribute_health()

results_iteration <- attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = list(11, 11, 10, 8, 7),
    bhd_central = list(4000, 2500, 3000, 1500, 500)
)

results_iteration <- attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)

Let’s check the main iteration results!

results_iteration$health_main

Let’s check the detailed iteration results!

results_iteration$health_detailed$impact_raw

Let’s check the comparison results!

results_comparison$health_main

monetize() health impacts

results_monetization <- 
  monetize(
    output_healthiar = results_pm_copd,
    discount_shape = "exponential",
    discount_rate = 0.03,
    discount_years = 5,
    valuation = 20)

Let’s check the monetize() output

results_monetization$monetization_main

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

openxlsx::write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results